Abstract
This article refines the way consumer confidence survey data are used in forecasting models. The refinement is easy to describe: it extends existing models by controlling for statistically significant changes in consumer confidence index values. The motivation behind this refinement is simply that not all changes in the confidence index are statistically significant, and mean index values alone provide a noisy signal. Using Michigan Index of Consumer Confidence from 1967 through 2013, we show that controlling for significant versus insignificant changes in the consumer confidence index materially enhances the explanatory power of household expenditure forecasting models.
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